Designing deep learning models can be a tall order without the right foundation.
Deep learning remains a field with enormous potential, yet so many practical applications don’t live up to expectations, as you can see from some of the main failures of AI you should know about.
We can lay the blame for most deep learning model failures on a surface-level understanding of DL frameworks. Consequently, it’s vital to choose the best deep learning courses online that teach you how it all works under the hood.
The right deep learning training will enable you to understand what you’re building, as opposed to only mastering a single framework, so you know how to troubleshoot your models when things don’t go according to plan.
In this guide, I’ll take you through the best deep learning courses and tutorials online in 2021 for building powerful and reliable deep learning models.
Let’s get started.
1. Deep Learning A-Z™: Hands-On Artificial Neural Networks [Udemy]
If you’d want to know how to build an artificial neural network from the ground up, then this might be a good course for you.
In this tutorial, you’ll get to perform image object detection by designing a Convolutional Neural Network (CNN).
You’ll also learn how to alter the files in the input folder so that your CNN can detect other objects of your preference.
Moreover, you’ll also get to create a powerful Recurrent Neural Network (RNN), to predict Google stock prices, before moving on to fraud detection models.
As a result, it’s one of the best deep learning courses on Udemy for its range of deep learning models.
However, because some of the code appears at the bottom of the screen, using captions can hinder the visibility of some of the code. Luckily, the audio quality is great so you can take the class without subtitles.
2. Neural Networks and Deep Learning [Coursera]
Are you completely new to the field of AI?
Then this is the course to get an overview of AI before moving on to deep learning.
For more exhaustive coverage on the foundations of artificial intelligence, you should give a try some of the best artificial intelligence courses online.
By the end of this course, you’ll be able to implement back and forward propagation to design a shallow neural network.
Afterward, you’ll get into neural network optimization, where you’ll use vectorization to boost your model’s speed. This makes it one of the best deep learning courses online if you’d want to design high-performing, deep learning models.
Notably, you may find that the assignments are a little straightforward as they entail filling in parts of already pre-written code. Nonetheless, the course’s simplicity makes it an excellent starting point if you’re new to deep learning and neural networks.
3. Complete Guide to TensorFlow for Deep Learning with Python [Udemy]
For a deep learning course involving Python and Google’s famous deep learning framework i.e. TensorFlow, this is the training to teach you how to use both to solve various machine learning problems.
After taking this course, it’ll be easy for you to create CNNs to classify images using TensorFlow, making it one of the best deep learning courses on Udemy for an introduction to TensorFlow.
If you’d like to specialize in deep learning design with TensorFlow, then you’re sure to also love some of the best TensorFlow courses online.
In the end, you’ll have the expertise to use OpenAI gym to create reinforcement learning algorithms with bias resistance.
Unfortunately, this course may be hard to follow along if you don’t have programming experience. However, it includes a crash course on Pandas and NumPy so you’ll have an idea of the Python skills needed for this course.
4. Deep Learning Specialization [Coursera]
This 5-part specialization covers not just deep neural networks, but will also take you through Natural Language Processing (NLP), another important branch of AI.
By the end of this tutorial, it’ll be clear how you can apply algorithms to video and image data, thereby enabling you to generate art via neural style transfer.
In addition to that, you’ll also learn algorithm optimization techniques such as variance analysis.
Other optimization methods you’ll cover include using HuggingFace tokenizers and working with word embeddings to train as well as build recurrent neural networks.
Unfortunately, you may find this is an overwhelming course if you aren’t well versed in Python, so you may want to first start with the best Python courses online.
However, it’s as a result one of the best deep learning courses on Coursera for leveraging Python for advanced deep learning applications.
5. Data Science: Deep Learning and Neural Networks in Python [Udemy]
Does designing a facial expression recognition system sound intriguing?
Then this is among the best deep learning courses online for creating artificial neural networks to achieve this specific goal.
This course will teach you backpropagation derivation, and using Python and Numpy to code a neural network.
What’s more, you’ll get to master the softmax function, which you can apply to develop neural networks with multi-class classification abilities (K >2 outputs).
With TensorFlow also covered from scratch, you’ll also learn how to use this tool to visualize model training.
This course is Calculus-based, so you may have trouble keeping up if your maths is rusty. Fortunately, you can easily brush up on that with some of the best probability & statistics courses online.
The upside to its heavily mathematical approach is that you get a better understanding of what you’re implementing.
6. DeepLearning.AI TensorFlow Developer Professional Certificate [Coursera]
Are you keen on getting the TensorFlow Developer Certificate?
This is one of the best deep learning courses on Coursera to help you prepare for the TensorFlow assessment exam that you need to pass to get it.
By the end of this course, you’ll have learned how to build NLP systems via Tensorflow.
In particular, you’ll gain the expertise to use text repositories to train specific RNNs such as the Long Short-Term Memory (LSTM) network.
You’ll also get to build and train CNNs in Keras. The networks will be able to classify images and create original poetry from real-world datasets.
Course support for this specialization isn’t timely, so you may have to rely on the discussion forums for assistance. However, the specialization is well-laid out and topics have been covered exhaustively so you shouldn’t run into too many problems.
7. Natural Language Processing (NLP) with Deep Learning in Python [Udemy]
If you have a specific interest in NLP, then this course has what you’re looking for.
It covers how you can use the skip-gram method for Word2Vec training to predict related words for a target word.
Building upon this knowledge, you’ll be able to implement named entity recognition by designing recurrent neural networks.
For a deep dive into other types of neural networks, you should check out the best neural networks courses on Udemy.
Furthermore, the tutorial cover tackles the CBOW method for guessing output from adjacent words, making this one of the best deep learning courses on Udemy for NLP applications as well.
Unfortunately, this is an advanced class that assumes calculus and Python experience, so you may find it hard to follow along without this experience. The good news is that it includes a free Numpy crash course to get you acquainted with Python.
8. Deep Learning Applications for Computer Vision [Coursera]
Computer vision is at the heart of many deep learning applications, and this is the course to comprehensively understand how it all works.
You’ll learn how to build a classic computer vision pipeline that you’ll put to use for image classification and object detection, as you tap into some of the top artificial intelligence trends in 2021.
This is followed up by learning to create a neural network for image classification predictions using TensorFlow.
The course also covers how you can use loss functions, which makes it one of the best deep learning courses online for making your deep learning models more reliable.
The computer vision applications covered in this course may appear too basic if you’re an advanced learner interested in solving challenging real-world problems. On the other hand, it serves as an excellent refresher and you may just learn new efficient ways to train your deep learning models.
9. Advanced AI: Deep Reinforcement Learning in Python [Udemy]
If you’re interested in advanced deep learning techniques such as Q-learning, you’ll find that this is the right training to show you the ropes.
The course also includes a Python for beginners crash course, which will get you in shape in terms of coding for Python 3, so you can better use Theano and TensorFlow.
As far as dealing with function approximation problems, this is among the best deep learning courses on Udemy as you get to build Radial Basis Function (RBF) for powerful reinforcement training.
Additionally, you’ll learn how to develop A3C, DQN, and other deep learning agents.
Unfortunately, most of the theory behind deep learning and neural networks is assumed and hence skipped. While this may make the teaching seem fast-paced, it proves an excellent advanced deep learning training that’s heavily focused on applied learning.
10. Deep Learning for Business [Coursera]
What are the business applications of deep learning?
This is the course to help you explore how deep learning is driving the business world.
After taking this course, you’ll be able to incorporate deep learning models into your business strategy for demand forecasting, making it one of the best deep learning courses on Coursera for business managers and entrepreneurs.
For more learning resources on business intelligence, you’ll find the best Coursera course business analytics to be very useful.
With a detailed section on recommender systems, you’ll also learn to create a recommendation engine using CNNs.
If you’re coming into this course from a technical background, you may feel that it superficially touches on deep learning. On the other hand, if you’re from a purely business background and don’t have a clue about AI, it’s a great option to make sense of deep learning fast and put it to work for you.
11. Modern Deep Learning in Python [Udemy]
How can you make artificial neural networks more stable and faster?
This course will teach you how to pair TensorFlow and Theano for batch normalization for better stability and speed.
As you get to create neural networks on PyTorch, MxNet, and CNTK, among others, it’s one of the best deep learning courses online because it covers most of the important frameworks and libraries.
On top of all that, you’ll also learn to leverage Amazon Web Services (AWS) to set up GPU instances and accelerate neural network training.
If you’d be interested in AWS in greater detail, be sure to also consider the best AWS courses online.
While code templates are not provided, this is a strategic teaching approach to enable you to write your own code as you go along and cultivate useful coding patterns.
12. Machine Learning Engineering for Production (MLOps) Specialization [Coursera]
From project scoping to development, this is the A-Z deep learning tutorial to take you through all the stages involved in building an end-to-end production system.
First, you’ll start by building the data pipelines for your models. This involves learning to source and remove dirty data from data sets.
Afterward, you’ll be able to prototype your deep learning algorithms and address concept drift to account for accuracy losses over time, making it one of the best deep learning courses on Coursera for maintaining a sustainable production system.
The specialization also entails proven data mining techniques for accurate modeling like feature engineering.
There’s a lot of preliminary introductions to TensorFlow, which you may find a little too long for comfort. However, you can easily speed through these sections but they are key to figuring out how to make the most of TensorFlow Extended in a production environment.
13. Deep Learning Prerequisites: Linear Regression in Python [Udemy]
Diving headfirst into deep learning model design without an understanding of linear regression can be overwhelming.
This is where this course steps in to give you the linear regression foundation needed, so you can unlock how to learn artificial intelligence in 2021.
Right from derivation, you’ll learn how to use Python to write your own linear regression model. Once you get a good grasp of the basics, you’ll move on to using gradient descent for linear regression.
For real-world practice, you’ll get to build a blood pressure prediction system using multi-dimensional linear regression, making this one of the best deep learning courses on Udemy for practically understanding linear regression models.
Without a foundation in probability and statistics, you may find that this course is a bit overwhelming. If you have your maths in order, it’s an excellent and comprehensive take on building linear regression models.
14. Introduction to Deep Learning & Neural Networks with Keras [Coursera]
Keras is an excellent API if you’re a Python programmer who’d like to learn how to develop deep learning models without too much coding.
After taking this deep learning training, you should have the expertise to use the Keras library to build deep learning models.
More specifically, you’ll also learn how you use the Restricted Boltzmann Machine (RBM), and autoencoders as well, in deep learning network design.
If you’re keen on also getting exposure to IBM Watson and how to use it to run and manage DL models, it is one of the best deep learning courses on Coursera to also get a feel of other important AI design tools.
While the course might appear elementary and brief, it does a great job of highlighting important concepts in Keras and IBM Watson so you can get a feel of areas you’d like to concentrate on.
15. Deep Learning Prerequisites: Logistic Regression in Python [Udemy]
Logistic regression is a vital technique in deep learning, statistics, and even data science.
If you’d like to understand how to code your own Python logistic regression module, then this is one of the best deep learning courses online for you.
By the end of this tutorial, you’ll have mastered how you can leverage regularisation when creating deep learning models to avoid creating complex algorithms that overfit training data.
When it comes to solving actual business problems, you’ll also grasp how logistic regression can come in handy for facial security systems and to forecast demand from unstructured, e-commerce data.
This course is quite wide, and maybe overwhelming because it covers other concepts outside logistic regression. As a result, it proves an excellent course to get an exhaustive background into Bayesian statistics, optimization theory, and other important statistics concepts you’ll need to push on into advanced neural network design.
To build powerful, accurate, and stable deep learning models, it’s important that you take the best deep learning courses online.
If you have basic Python programming skills and are interested in a beginner-friendly tutorial, you should consider the Deep Learning A-Z™: Hands-On Artificial Neural Networks course.
Conversely, if you have intermediate to advanced python programming experience, then you should feel comfortable with any of the best deep learning courses and tutorials online in 2021.
In particular, I recommend the Neural Networks and Deep Learning training.
It is a comprehensive 5-part specialization that will teach you how to design deep neural networks.
Lerma is our expert in online education with over a decade of experience. Specializing in e-learning and e-courses. She has reviewed several online training courses and enjoys reviewing e-learning platforms for individuals and organizations.